Prospective prediction and control of image properties in model-based material decomposition for spectral CT

Wenying Wang, Matthew Tivnan, Grace J. Gang, J. Webster Stayman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties of material basis volumes can be complex. For example, spatial resolution, noise, and cross-talk can depend on acquisition parameters, regularization, patient size, and anatomical target. In this work, we propose a set of prospective prediction tools for the generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, as well as noise correlation. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationPhysics of Medical Imaging
EditorsGuang-Hong Chen, Hilde Bosmans
ISBN (Electronic)9781510633919
StatePublished - 2020
EventMedical Imaging 2020: Physics of Medical Imaging - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2020: Physics of Medical Imaging
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Prospective prediction and control of image properties in model-based material decomposition for spectral CT'. Together they form a unique fingerprint.

Cite this